In this paper, we propose a novel method, Deep Feature Proxy Variable (DFPV) method, which is a first work to apply neural networks to the PCL problem. The technique we employ builds on earlier work in instrumental variable (IV) regression, which is a related causal inference setting to PCL. A range of deep learning methods have recently been introduced for IV regression [1]}, [2]}, [3]}. We propose to adapt the Deep Feature Instruemtal Variable method [2]}, which learns deep adaptive features within a two-stage regression framework. Details of DFPV are given in Section . In our Section  experiments, we empirically show that DFPV performs better than other PCL methods in several examples. We further apply PCL methods to the off-policy evaluation problem in a confounded bandit setting, in which DFPV performs best among competing PCL methods.
